Absenteeism At Work
Li Wen, Stephanie Yow
Photo by Pixabay from Pexels
1. Costs and Causes of Absenteeism
Forbes estimates the average cost of absenteeism to be $4,080 per full-time employee and $2,040 per part-time employee annually (Kelly 2024).
According to the Academy to Innovate HR (Vulpen 2020),
Absenteeism is any failure to report for or remain at work as scheduled, regardless of the reason… [the key is] the person was scheduled to work. This means that absenteeism does not include [planned] vacation, personal leave, jury-duty leave, or other reasons.
What gives rise to absenteeism? Studies show that poor health is a significant contributing cause (Nawata 2024). For example, histories of heart and kidney disease and large weight increments are positively associated with absenteeism.
2. Report Objectives
For this report, we assume that a company’s human resource department wants to look into the state of absenteeism among its employees. They have a suspicion that absenteeism due to poor health costs the most to the company, and would like confirmation supported by data. In particular, they wish to know if employees with a higher risk for poor health exhibited more absenteeism than others.
Objectives are to:
Analyse the distribution and variability of absenteeism time
Demonstrate the applicability of the Central Limit Theorem to absenteeism time
Analyse reasons cited for absence by frequency and absenteeism time
Analyse health and absenteeism by comparing observations between all absentees and various samples, using body mass index (BMI) as a health indicator
Please refer to the Appendix for a checklist of project requirements.
3. About the Dataset
From July 2007 to July 2010, 740 absenteeism records were collected at a non-fictional courier company in Brazil for research purposes (Andrea Martiniano 2012), and then donated to UC Irvine’s Machine Learning Repository in 2018.
Attributes comprise (1) absenteeism time in hours and reasons cited for absence; (2) day, month and season of absence; (3) job-related attributes; and (4) personal attributes. Documentation on attributes can be found here.
Some discrete numerical values are encoded categorical values. For
example, values 1-21 under Reason.for.absence refer to
disease classification codes established in the International Classification of Diseases 10th
Revision.
Although absenteeism records were collected over 3 years, the dataset does not have a ‘year’ attribute, for reasons unknown. Each record refers to absenteeism exhibited by an employee within a month. An employee may have more than one record. For example, in the first record, employee ID 11 was absent in July (year unknown) for 4 hours, and then absent in July again for 2 hours in the 5th record.
While the dataset does not have missing values, it has anomalous 0
values for attributes that should not have 0 values. For example,
Reason.for.absence should only contain values >0 based
on dataset documentation, but has a 0 value in the 2nd record.
3.1 Preprocessing Aspects
There are 5 main aspects to preprocess:
Standardise column naming convention
Select columns relevant to report objectives
Remove anomalous 0 values from columns that should not have 0 values
Replace encoded categorical values with actual meanings based on documentation
Classify BMI using World Health Organization criteria for adults, i.e. underweight (<18.5), healthy weight (18.5 to <25), overweight (25 to <30), and obesity (30+), to aid sampling and analysis
# standardise column naming convention
colnames(ab) |>
str_to_lower() |>
str_replace_all(pattern = '\\.', replacement = '_') -> colnames(ab)
# select relevant columns
# remove anomalous 0 values
ab |>
select(id, absenteeism_time_in_hours, body_mass_index, reason_for_absence) |>
filter(reason_for_absence != 0, absenteeism_time_in_hours != 0) -> ab
# replace encoded categorical values
for (i in 1:length(ab$reason_for_absence)) {
if (ab$reason_for_absence[i] %in% 1:21) {
ab$reason_unencoded[i] <- 'Disease'
} else if (ab$reason_for_absence[i] == 22) {
ab$reason_unencoded[i] <- 'Patient Follow-Up'
} else if (ab$reason_for_absence[i] == 23) {
ab$reason_unencoded[i] <- 'Medical Consultation'
} else if (ab$reason_for_absence[i] == 24) {
ab$reason_unencoded[i] <- 'Blood Donation'
} else if (ab$reason_for_absence[i] == 25) {
ab$reason_unencoded[i] <- 'Laboratory Examination'
} else if (ab$reason_for_absence[i] == 26) {
ab$reason_unencoded[i] <- 'Unjustified Absence'
} else if (ab$reason_for_absence[i] == 27) {
ab$reason_unencoded[i] <- 'Physiotherapy'
} else if (ab$reason_for_absence[i] == 28) {
ab$reason_unencoded[i] <- 'Dental Consultation'
} else {
ab$reason_unencoded[i] <- 'Unjustified Absence'
}
}
# classify body mass index
# no observed records of employees who are underweight (bmi < 18.5)
for (i in 1:length(ab$body_mass_index)) {
if (ab$body_mass_index[i] >= 18.5
& ab$body_mass_index[i] <= 24.9) {
ab$bmi_category[i] <- 'Healthy Weight'
} else if (ab$body_mass_index[i] >= 25.0
& ab$body_mass_index[i] <= 29.9) {
ab$bmi_category[i] <- 'Overweight'
} else {
ab$bmi_category[i] <- 'Obesity'
}
}
# cast new column bmi_category as factor
ab$bmi_category <- factor(ab$bmi_category,
levels = c('Healthy Weight', 'Overweight', 'Obesity'))
# rearrange columns
ab |>
select(id, absenteeism_time_in_hours, reason_for_absence,
reason_unencoded, everything()) -> ab
datatable(ab,
caption = 'After preprocessing: 696 rows x 6 columns',
options = list(searching = FALSE)
)4. State of Absenteeism
4.1 Distribution and Variability of Absenteeism Time
Observed distribution of absenteeism time is right-skewed. 417 (59.9%) of 696 records are in the [0,5) hours range. 216 (31.0%) records are in the [5,10) hours range. Remaining <10% of records are at least 15 hours.
ab |>
plot_ly(x = ~absenteeism_time_in_hours, type = 'histogram', alpha = 0.8,
showlegend = FALSE, xbins = list(start = 0, end = 125, size = 5)) |>
layout(title = 'Distribution of Absenteeism Time',
xaxis = list(title = 'Absenteeism Time (Hours)',
ticktext = format(seq(0, 125, by = 5)),
tickvals = seq(0, 125, by = 5), tickmode = 'array',
range = c(0, 125)),
yaxis = list(title = 'Frequency', range = c(0, 450)),
bargap = 0.005)Observed median absenteeism time is 3 hours. Bottom 25% of records had 2 hours or less of absenteeism time. Bottom 75% of records had 8 hours or less of absenteeism time. In other words, the variation of absenteeism time between the 25th and 75th percentile of records (middle 50%) was 6 hours.
Records with an absenteeism time of >16 hours may be considered outliers. Highest number of hours absent for any employee was 120 (equivalent to three 40-hour work weeks). Lowest number of hours was 1, indicating a wide range.
While it may be tempting to omit outliers to lessen variability in the dataset, they are legitimate observations of absenteeism, and depict the real-world nature of unplanned and prolonged situations that affect our ability to work.
4.2 Applicability of the Central Limit Theorem to Absenteeism Time
Despite a right-skewed distribution, given a sufficiently large sample size, the distributions of sample means for absenteeism time will approximate or converge to a normal distribution.
In order to demonstrate the theorem, we set sample sizes of 30, 60, 90 and 120, and draw 1,000 samples without replacement per sample size. Means of 1,000 samples are plotted in a density histogram, one histogram per sample size.
set.seed(138)
n_samples <- 1000
sample_size <- c(30, 60, 90, 120)
xbar <- numeric(length = n_samples)
plot_list <- list()
plot_tib <- tibble()
for (i in 1:length(sample_size)) {
size <- sample_size[i]
for (j in 1:n_samples) {
xbar[j] <- mean(sample(ab$absenteeism_time_in_hours, size = size,
replace = FALSE))
}
plot <- plot_ly(
x = xbar,
type = 'histogram',
name = paste('n =', size),
histnorm = 'probability density'
) |>
layout(
xaxis = list(title = 'Absenteeism Time (Hours) Sample Mean',
range = c(0, 20)),
yaxis = list(title = 'Density', range = c(0, 0.4))
)
plot_list[[i]] <- plot
plot_tib <- rbind(plot_tib,
tibble(
'Distribution' = paste('Sample Means (n = ', size, ')', sep = ''),
'Mean' = round(mean(xbar), 2),
'Standard Deviation' = round(sd(xbar), 2),
'Theoretical Standard Deviation' =
round(sd(ab$absenteeism_time_in_hours)/sqrt(size), 2)
)
)
}
subplot(
plot_list,
nrows = 2,
shareX = TRUE,
shareY = TRUE,
titleX = TRUE,
titleY = TRUE
) |>
layout(
title = 'As sample size n increases, sample means distributions converge to
a normal distribution',
showlegend = TRUE
)plot_tib <- rbind(tibble('Distribution' = 'Input Data',
'Mean' = round(mean(ab$absenteeism_time_in_hours), 2),
'Standard Deviation' =
round(sd(ab$absenteeism_time_in_hours), 2),
'Theoretical Standard Deviation' =
round(sd(ab$absenteeism_time_in_hours), 2)
), plot_tib
)
knitr::kable(plot_tib,
caption = 'Comparison of input data and sample means distributions')| Distribution | Mean | Standard Deviation | Theoretical Standard Deviation |
|---|---|---|---|
| Input Data | 7.36 | 13.63 | 13.63 |
| Sample Means (n = 30) | 7.43 | 2.52 | 2.49 |
| Sample Means (n = 60) | 7.24 | 1.66 | 1.76 |
| Sample Means (n = 90) | 7.27 | 1.33 | 1.44 |
| Sample Means (n = 120) | 7.37 | 1.11 | 1.24 |
A few observations can be made:
As sample size n increases, means of sample means distributions converge to the mean of the input data (7.36 hours).
Standard deviations and theoretical standard deviations of sample means distributions are similar. Theoretical values are derived using standard deviation of input data / square root of n, where n is the sample size.
Spreads of sample means distributions are narrower than the spread of input data, and have lower standard deviations than input data.
As sample size n increases, standard deviation decreases, and the spreads of sample means distributions become narrower.
5. Reasons Cited For Absence
In this section, reasons cited for absence shall be analysed by frequency and mean absenteeism time. From the results, we can either confirm or dismiss the suspicion that absenteeism due to poor health costs the most to the company.
5.1 By Frequency
Disease was the most frequent reason for absence (262 occurrences, or 37.6% of total). Medical consultation was the second most frequent (149 occurrences, or 21.4% of total).
as.data.frame(sort(table(ab$reason_unencoded), descending = TRUE)) |>
plot_ly(y = ~Var1, x = ~Freq, type = 'bar', orientation = 'h') |>
layout(title = 'Reasons For Absence',
xaxis = list(title = 'Frequency',
ticktext = format(seq(0, 300, by = 50)),
tickvals = seq(0, 300, by = 50), tickmode = 'array',
range = c(0, 300)),
yaxis = list(title = ''))5.2 By Mean Absenteeism Time
Absences due to disease, blood donation and patient follow-up had the highest mean times, exceeding the total mean of 7.36 hours. Furthermore, absences due to disease had a mean time of 13.52 hours.
ab |>
group_by(reason_unencoded) |>
summarise(mean_absenteeism_time = round(mean(absenteeism_time_in_hours), 2)) |>
arrange(mean_absenteeism_time) -> T1
T1$reason_unencoded <- factor(T1$reason_unencoded, levels = T1$reason_unencoded)
T1 |>
plot_ly(x = ~mean_absenteeism_time, y = ~reason_unencoded, type = 'bar',
orientation = 'h') |>
layout(title = 'Reasons For Absence',
xaxis = list(title = 'Mean Absenteeism Time (Hours)',
range = c(0, 14)),
yaxis = list(title = ''))Indeed, disease was the most frequently cited and most time-consuming reason for absence. Since time is money where salaries are concerned, absences due to disease could be said to cost the most to the company.
5.3 By Type of Disease
Among records that cited disease as a reason, the most frequent disease type was disease of the musculoskeletal system and connective tissue. According to the International Classification of Diseases 10th Revision, conditions such as osteoarthritis is included in this broad type.
T2 <- table(subset(ab, reason_for_absence %in% 1:21)$reason_for_absence)
as.data.frame(sort(T2, decreasing = TRUE)) |>
plot_ly(y = ~Freq, x = ~Var1, type = 'bar') |>
layout(title = 'Types of Diseases Causing Absenteeism',
yaxis = list(title = 'Frequency',
range = c(0, 60)),
xaxis = list(title = 'International Classification of Diseases 10th Revision (ICD-10) Code'))knitr::kable(
tibble(
'Code' = names(sort(T2, decreasing = TRUE)[1:5]),
'Description' = c('Diseases of the musculoskeletal system and connective tissue',
'Injury, poisoning and certain other consequences of external causes',
'Diseases of the digestive system',
'Diseases of the respiratory system',
'Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified')
),
'caption' = 'Top 5 disease types causing absenteeism by ICD-10 code and description'
)| Code | Description |
|---|---|
| 13 | Diseases of the musculoskeletal system and connective tissue |
| 19 | Injury, poisoning and certain other consequences of external causes |
| 11 | Diseases of the digestive system |
| 10 | Diseases of the respiratory system |
| 18 | Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified |
6. Health and Absenteeism
People who have overweight and obesity (BMI ≥ 25) have an increased risk for diseases and health conditions (CDC 2025), compared to people with healthy weight. Risks include early mortality, high blood pressure, type 2 diabetes, coronary heart disease, stroke, cancer, and osteoarthritis.
Using BMI as a health indicator, we shall attempt to answer the question:
Did employees with a higher risk for poor health exhibit more absenteeism than others?
This report shall adopt people-first language that is encouraged by the US CDC, which means instead of using descriptions that may carry stigma such as ‘obese employees’, it uses people-first descriptions such as ‘employees who have/with obesity/overweight’.
6.1 Observations
BMI distribution across all employees appears to follow the shape of an almost normal distribution, as it shows some symmetry around the mean (26.0), and the mean is close to the median (25) and the mode (25).
Furthermore, 69.70% of values fall within one standard deviation of the mean, 93.94% within 2 standard deviations of the mean, and 100% within 3 standard deviations from the mean (due to the limited number of values), close to the spread of a normal distribution.
ab |>
group_by(id) |>
summarise(bmi_by_id = mean(body_mass_index)) -> T3
mean_T3 <- mean(T3$bmi_by_id)
sd_T3 <- sd(T3$bmi_by_id)
within_1sd <- nrow(subset(T3, bmi_by_id > mean_T3-sd_T3
& bmi_by_id < mean_T3+sd_T3
)) / nrow(T3)*100
within_2sd <- nrow(subset(T3, bmi_by_id > mean_T3-sd_T3*2
& bmi_by_id < mean_T3+sd_T3*2
)) / nrow(T3)*100
within_3sd <- nrow(subset(T3, bmi_by_id > mean_T3-sd_T3*3
& bmi_by_id < mean_T3+sd_T3*3
)) / nrow(T3)*100
knitr::kable(
tribble(
~Spread, ~`BMI Distribution`, ~`Normal Distribution`,
'(μ-σ, μ+σ)', sprintf('%.2f%% of values', within_1sd), '68.27% of values',
'(μ-2σ, μ+2σ)', sprintf('%.2f%%', within_2sd), '95.45%',
'(μ-3σ, μ+3σ)', sprintf('%.0f%%', within_3sd), '99.73%'
),
caption = 'Comparison of BMI and normal distributions'
)| Spread | BMI Distribution | Normal Distribution |
|---|---|---|
| (μ-σ, μ+σ) | 69.70% of values | 68.27% of values |
| (μ-2σ, μ+2σ) | 93.94% | 95.45% |
| (μ-3σ, μ+3σ) | 100% | 99.73% |
As one employee may have more than one absenteeism record, records were grouped by unique employee first, and then summarised by mean BMI, in case of fluctuations in weight over 3 years.
median_T3 <- median(T3$bmi_by_id)
mode_T3 <- names(sort(table(T3$bmi_by_id), decreasing = TRUE)[1])
T3 |>
plot_ly(x = ~bmi_by_id, type = 'histogram', alpha = 0.8,
showlegend = FALSE, xbins = list(start = 15, end = 40, size = 5)) |>
layout(
shapes = list(
list(
type = 'line',
opacity = 0.8,
y0 = 0, y1 = 245,
x0 = 26, x1 = 26,
line = list(color = "red", width = 2, dash = "dash")
),
list(
type = 'line',
opacity = 0.8,
y0 = 0, y1 = 245,
x0 = 25, x1 = 25,
line = list(color = "green", width = 2, dash = "dash")
)
),
annotations = list(
list(
x = 27.6, y = 14.5, text = paste('Mean =', round(mean_T3, 1)), showarrow = FALSE
),
list(
x = 23.4, y = 14.5, text = paste('Median =', median_T3), showarrow = FALSE
),
list(
x = 23.6, y = 13.5, text = paste('Mode =', mode_T3), showarrow = FALSE
)
),
title = paste('Distribution of Body Mass Index (BMI)'),
xaxis = list(title = 'Mean Body Mass Index (BMI)',
ticktext = format(seq(15, 40, by = 5)),
tickvals = seq(15, 40, by = 5), tickmode = 'array',
range = c(15, 40)),
yaxis = list(title = 'Frequency', range = c(0, 15)),
bargap = 0.005
)Where absenteeism time is concerned, a look at the scatter plot of BMI and absenteeism time of all records suggests that while there is no observable linear relationship, absenteeism time outliers (>16 hours) appear to cluster around BMI of 25, which falls in the ‘overweight’ category.
ab |>
plot_ly(y = ~absenteeism_time_in_hours, x = ~body_mass_index,
size = ~absenteeism_time_in_hours,
type = 'scatter', mode = 'markers') |>
layout(shapes = list(type = 'rect', fillcolor = 'red',
line = list(color = "red"),
opacity = 0.2,
y0 = 0, y1 = 122,
x0 = 24.5, x1 = 40),
title = 'Did records associated with BMI ≥ 25 exhibit more absenteeism?',
xaxis = list(title = 'Body Mass Index (BMI)',
range = c(15, 40)),
yaxis = list(title = 'Absenteeism Time (Hours)', range = c(0, 130)))Based on mean absenteeism time, employees with overweight exhibited more absenteeism (8.99 hours) than others. Mean absenteeism time of employees with obesity (6.76 hours) was lower than that of employees with healthy weight (8.17 hours).
As one employee may have more than one absenteeism record, mean absenteeism time per unique employee was calculated first, and then an average of all mean absenteeism times of unique employees within each BMI category was taken. There were no employees in the ‘underweight’ category, hence this category was not shown for comparison.
ab |>
group_by(bmi_category, id) |>
summarise(mean_absence = round(mean(absenteeism_time_in_hours), 2)) |>
group_by(bmi_category) |>
summarise(mean_absence_bmi = round(mean(mean_absence), 2)) |>
plot_ly(x = ~mean_absence_bmi, y = ~bmi_category, type = 'bar',
orientation = 'h') |>
layout(title = 'Employees with overweight exhibited more absenteeism than others',
xaxis = list(title = 'Mean Absenteeism Time (Hours)',
range = c(0, 10)),
yaxis = list(title = ''))Observations so far indicate that employees with a higher risk for poor health did not conclusively exhibit more absenteeism than others, as that statement was not consistent for employees with obesity who are at higher risk for poor health, according to the US CDC.
6.2 Would Conclusions Change Under Different Sampling Methods?
This section continues to examine health and absenteeism time by drawing samples from input data. There are 4 samples:
Cited disease as a reason (n = 262, actual count): subset of all records; only records that state ‘Disease’ under
reason_unencodedare retrievedSimple random sample without replacement (n = 262)
Systematic sample based on unequal probabilities (n = 262): proportional-to-size inclusion probabilities are generated based on absenteeism time
Stratified sample (n = 262): strata proportionally represents size of BMI categories and size of absenteeism records of unique employees, and the sample is taken using simple random sampling without replacement
A sample size of 262 was chosen for samples drawn using sampling methods to be consistent with the size of the subset.
A look at the distribution of absenteeism time by sample shows that the stratified sample is most representative of input data by having the closest mean (7.23 hours), the same median (3 hours) and the same interquartile range (6 hours). Simple random sample is next most representative of input data by having a close mean (7.83 hours), a close median (4 hours) and the same interquartile range (6 hours).
sample_size <- 262
# cited disease as a reason
ab |>
filter(reason_unencoded == 'Disease') -> ab1
ab1 |>
group_by(bmi_category, id) |>
summarise(mean_absence = round(mean(absenteeism_time_in_hours), 2)) |>
group_by(bmi_category) |>
summarise(mean_absence_bmi = round(mean(mean_absence), 2)) |>
plot_ly(x = ~mean_absence_bmi, y = ~bmi_category, type = 'bar',
orientation = 'h', name = 'Cited Disease As Reason') |>
layout(xaxis = list(title = 'Mean Absenteeism Time (Hours)',
range = c(0, 20)),
yaxis = list(title = '')) -> plot1
# simple random sampling without replacement
set.seed(138)
s1 <- srswor(sample_size, nrow(ab))
s1 <- ab[s1 != 0, ]
s1 |>
group_by(bmi_category, id) |>
summarise(mean_absence = round(mean(absenteeism_time_in_hours), 2)) |>
group_by(bmi_category) |>
summarise(mean_absence_bmi = round(mean(mean_absence), 2)) |>
plot_ly(x = ~mean_absence_bmi, y = ~bmi_category, type = 'bar',
orientation = 'h', name = 'Simple Random (WOR)') |>
layout(xaxis = list(title = 'Mean Absenteeism Time (Hours)',
range = c(0, 20)),
yaxis = list(title = '')) -> plot2
# unequal probability systematic sampling
set.seed(138)
pik <- inclusionprobabilities(ab$absenteeism_time_in_hours, sample_size)
s2 <- UPsystematic(pik)
s2 <- ab[s2 != 0, ]
s2 |>
group_by(bmi_category, id) |>
summarise(mean_absence = round(mean(absenteeism_time_in_hours), 2)) |>
group_by(bmi_category) |>
summarise(mean_absence_bmi = round(mean(mean_absence), 2)) |>
plot_ly(x = ~mean_absence_bmi, y = ~bmi_category, type = 'bar',
orientation = 'h', name = 'UP Systematic') |>
layout(xaxis = list(title = 'Mean Absenteeism Time (Hours)',
range = c(0, 20)),
yaxis = list(title = '')) -> plot3
# stratified sampling
set.seed(138)
ab |>
arrange(bmi_category, id) -> ab
freq <- table(ab$bmi_category, ab$id)
strata_size <- round(sample_size * freq / sum(freq))
strata_size <- as.vector(t(strata_size))
strata_size <- strata_size[strata_size != 0] # sum is 265
# make adjustment to sum of 262
strata_size[c(11, 26, 28)] <- strata_size[c(11, 26, 28)] - 1
s3 <- sampling::strata(ab, stratanames = c('bmi_category', 'id'),
size = strata_size, method = c('srswor'),
description = FALSE)
s3 <- getdata(ab, s3)
s3 |>
group_by(bmi_category, id) |>
summarise(mean_absence = round(mean(absenteeism_time_in_hours), 2)) |>
group_by(bmi_category) |>
summarise(mean_absence_bmi = round(mean(mean_absence), 2)) |>
plot_ly(x = ~mean_absence_bmi, y = ~bmi_category, type = 'bar',
orientation = 'h', name = 'Stratified') |>
layout(xaxis = list(title = 'Mean Absenteeism Time (Hours)',
range = c(0, 20)),
yaxis = list(title = '')) -> plot4
# distribution of absenteeism time by sample
ab |>
plot_ly(y = ~absenteeism_time_in_hours, type = 'box',
name = 'Input Data', boxmean = TRUE) |>
add_boxplot(data = ab1, y = ab1$absenteeism_time_in_hours,
name = 'Cited Disease As Reason', boxmean = TRUE) |>
add_boxplot(data = s1, y = s1$absenteeism_time_in_hours,
name = 'Simple Random (WOR)', boxmean = TRUE) |>
add_boxplot(data = s2, y = s2$absenteeism_time_in_hours,
name = 'UP Systematic', boxmean = TRUE) |>
add_boxplot(data = s3, y = s3$absenteeism_time_in_hours,
name = 'Stratified', boxmean = TRUE) |>
layout(title = 'Stratified and simple random samples are most representative of input data',
xaxis = list(title = '', tickangle = -90),
yaxis = list(title = 'Absenteeism Time (Hours)'))For the stratified sample, similar to that observed for input data, only employees with overweight exhibited higher mean absenteeism time (8.55 hours) than others. Employees with obesity exhibited the least mean absenteeism time (6.78 hours).
For the simple random sample, mean absenteeism time of employees with overweight (9.3 hours) was just shy of employees with healthy weight (9.77 hours). Employees with obesity exhibited the least mean absenteeism time (5.95 hours).
For the unequal probability systematic sample, only employees with overweight exhibited higher mean absenteeism time (15.12 hours) than others. Employees with obesity exhibited the least mean absenteeism time (10.5 hours). Higher overall means were due to higher absenteeism times being given weight in inclusion probabilities.
When disease was cited as a reason for absence, mean absenteeism time across BMI categories increased relative to the stratified and simple random samples.
Only employees with overweight exhibited higher mean absenteeism time (13.47 hours) than others. Mean absenteeism time of employees with obesity (11.16 hours) was almost on par with employees of healthy weight (11.27 hours). When employees with obesity were absent due to disease, their absenteeism became more pronounced than when they were absent for a variety of reasons including disease in other samples.
subplot(
list(plot1, plot2,
plot3, plot4),
nrows = 2,
shareX = TRUE,
shareY = TRUE,
titleX = TRUE,
titleY = TRUE
) |>
layout(
title = 'Employees with overweight continued to exhibit more absenteeism than others',
showlegend = TRUE
)Using BMI as a health indicator, employees with a higher risk for poor health did not conclusively exhibit more absenteeism than others. Reason for absence mattered too.
7. Report Summary
Through this report, we learned that:
Median absenteeism time was 3 hours.
Mean absenteeism time was 7.36 hours.
Between the 25th percentile (2 hours) and 75th percentile (8 hours) of records, absenteeism time varied by 6 hours.
Despite a non-normal distribution, sample means distributions for absenteeism time approximated or converged to a normal distribution, as the sample size grew.
Disease was the most frequently cited and most time-consuming reason for absence. Since time is money where salaries are concerned, absences due to disease could be said to cost the most to the company.
Disease of the musculoskeletal system and connective tissue was the most frequently cited disease type.
BMI distribution of employees followed the shape of an almost normal distribution.
Employees with a higher risk for poor health did not conclusively exhibit more absenteeism than others. Reason for absence mattered too.
If the human resource department wishes to investigate absenteeism more by looking into a smaller dataset, the stratified or simple random sample may be used as they are most representative of absenteeism in the company.
8. References
9. Appendix
9.1 Checklist of Project Requirements
| Requirement | Section |
|---|---|
| Data Import and Preprocessing | 3 |
| Univariate Analysis (Categorical) | 5.1, 5.3 |
| Univariate Analysis (Numerical) | 4.1, 6.1 |
| Bivariate Analysis | 5.2, 6.1, 6.2 |
| Numerical Variable Distribution | 4.1, 6.1 |
| Applicability of Central Limit Theorem | 4.2 |
| Sampling Methods | 6.2 |
| Data Wrangling (dplyr, stringr, tibble) | Entire report |
| Use of Plotly | Entire report |
Questions? Please contact Stephanie Yow.
This report was written as a final project of Professor Suresh Kalathur’s graduate-level course, CS544 Foundations of Analytics and Data Visualization, conducted during Fall 2025 at the Metropolitan College Department of Computer Science, Boston University, USA.